Data Fusion & Resource Management (DF&RM) Dual Node ......Hypothesis Evaluation Christopher Bowman,...

transcript

Data Fusion & Resource Management (DF&RM) Dual Node

Network (DNN) Association Hypothesis Evaluation

Christopher Bowman, PhD.

Data Fusion & Neural Networks (DF&NN)

Sept 2020

Briefing Objectives

• Provide an understanding of the roles for Data Fusion & Resource

Management (DF&RM)

• Describe how the Data Fusion heritage can be used to “jump-start” dual

Resource Management solutions

• Describe DF&RM Dual Node Network (DNN) Technical Architecture

• Provide Problem-to-Solution Mappings for Data Association

• Provide Baseline Max A Posteriori (MAP) Data Association Hypothesis

Evaluation Equations

AGENDA

❖DF&RM Dual Node Network (DNN) Technical Architecture

➢Distributed Data Fusion Node Networks

➢Data Association Hypothesis Evaluation Alternatives

Fusion & Management Lie in the Gap Between “Observe” and “Act”

• Data Fusion is the process of combining data/information to

estimate or predict the state of some aspect of the world.

• Resource Management is the process of planning/

controlling response capabilities to meet mission objectives

E

N

V

I

R

O

N

M

E

N

T

Sensors/

Sources

Data

Fusion

Resource

Management

U

S

E

R

I

N

T

E

R

F

A

C

E

Response

Systems

“Observe” “Orient”

“Act” “Decide”

Sensor Fusion Exploits Sensor Commonalities and Differences

Data Association Uses Overlapping Sensor Capabilities so that State Estimation Can Exploit their Synergies

CONFIDENCECOVERT

COVERAGE

DETECTION

RANGE ANGLE

KINEMATICS

CLASS TYPE

CLASSIFICATION

POOR GOOD FAIR FAIR POOR

FAIR POOR GOOD FAIR FAIR

FAIR FAIR FAIR FAIR GOOD GOOD

RADAR

EO/IR

C3I

GOOD GOOD GOOD GOOD GOOD GOOD

DataPreparation

DataAssociation

StateEstimation

DataFusion

FAIR

FAIR

RADAR

EO/IR

SIGINT

TARGET ORENTITY OFINTEREST

Resource Management Exploits Sensor Commonalities & Differences(Sensor Management Example)

Sensor Task Planning Uses Overlapping Sensor Capabilities so that Control Can Exploit their Synergies

CONFIDENCECOVERT

COVERAGE

DETECTION

RANGE ANGLE

KINEMATICS

CLASS TYPE

CLASSIFICATION

POOR GOOD FAIR FAIR POOR

FAIR POOR GOOD FAIR FAIR

FAIR FAIR FAIR FAIR GOOD GOOD

RADAR

EO/IR

C3I

ResourceManagement

FAIR

FAIR

TARGET

TaskPreparation

TaskPlanning

Tasking/Control

GOOD GOOD GOOD GOOD GOOD GOOD

DataPreparation

DataAssociation

StateEstimation

DataFusion

2004 Revision of the Joint Director’s Lab Data Fusion Model

ResourceMgmtExternal

Distributed

Local

INTELEW

SONARRADAR

.

.

.Data

bases

SOURCES

Database ManagementSystem

SupportDatabase

FusionDatabase

Human/ComputerInterface

Level 4Processing

SYSTEM

ASSESSMENT

DATA FUSION DOMAIN

Level 1Processing

ENTITYASSESSMENT

Level 2Processing

SITUATIONASSESSMENT

Level 3Processing

IMPACTASSESSMENT

Level 0Processing

SIGNAL/FEATURE

ASSESSMENT

Using a Fusion & Management Architecture Will Stop One-of-a-Kind Software Developments

• Architectures are frequently used mechanisms to address a broad range of common requirements to achieve interoperability and affordabilityobjectives

• An architecture (IEEE definition) is a structure of components, their relationships, and the principles and guidelines governing their design and evolution over time

• An architecture should:• Identify a focused purpose with sufficient breadth to achieve affordability objectives

• Facilitate user understanding/communication

• Permit comparison, integration, and interoperability

• Promote expandability, modularity, and reusability

• Achieve most useful results with least cost of development

Role for DF&RM DNN Technical Architecture Within the “DoD Architecture Framework”

• The operational architecture provides the “what and who” operational needs

• The technical architecture provides “problem-to-solution space” guidance

• The systems architecture defines the “how” to build the operational system

DF&RM DNN Technical Architecture Applies at Application Layer

Registration

H/W Environment Executive S/W Environment

V_OODB Database: Source Data, Fused Products + Libraries

Core Information Environment

ISR Tactical Operations

Management

User Interface

& Visualization

Model Services Metrics

Data Services: Pedigrees, Access, Query

Apps

Resource

s

Sensors

Resource

s

Sensors ISR Tactical

Operatio

ns

Fusion

Common Interface: APIs, etc.

Data Mining Provides DF&RM Models

➢ Data Mining discovers and models some as aspect of data input to each fusion level

➢ Data Fusion combines data to estimate/predict the desired state at each fusion level

Space

Situation

Tracks

Data Mining

Parameters

TrnSat &

TP3

Measu

remen

ts &

SOH

Level 0 Mining & Fusion

AbNet

L0 Fusion

L0 Mining

Parameters

ClassCat &

Trigger Det

Abnormality

Detections


CleverSet

L1 Event

Tracking

On -line

SOH

NN Weights,

Signature

Models,

& Cause

Taxonomy

Tracking

Triggers

& Event

Models

L1 Mining

Parameters

TemPats &

Track Viewer


CRA L2

Situation

Tracking

Relationship

Ontology

& Situation

Models

Abnormal

Event Tracks

L2 Mining

Parameters

Analyst

L2 Viewer


L3 Impact

Prediction

Course

of Action

& Impact

Models

Mission

Impacts

Data Mining

Data Fusion

Space

Situation

Tracks

Data Mining

Parameters

TrnSat &

Supervisor

Measu

remen

ts &

SOH


AbNet

L0 Fusion

L0 Mining

Parameters

ClassCat &

Trigger Det

Abnormality

Detections


L1 Event

Tracking

On -line

SOH

NN Weights,

Signature

Models,

& Cause

Taxonomy

Tracking

Triggers

& Event

Models

L1 Mining

Parameters

Smoking Gun

Track Viewer


L2

Situation

Tracking

Relationship

Ontology

& Situation

Models

Abnormal

Event Tracks

L2 Mining

Parameters

Analyst

L2 CTP


L3 Impact

Prediction

Course

of Action

& Impact

Models

Mission

Impacts

Data Mining

Data Fusion

Fusion Network Selected to Balance Performance & Complexity

Single Platform

Single Sensor

Single Time

Single Data

Type

Least

Complex

Tree

Knee-of-the Curve

Fusion Tree

All Platforms

All Sensors/Sources

All Past Times

All Data Types

Best

Performance

Tree

Data Fusion

Performance

Data Fusion Cost/Complexity

“Knee of Curve” Design

DF&RM Trees Divide & Conquer the Problem

ResourcesResources

RESOURCE MGMT

Management Architecture

• “Fan-out” Tree

• Task batching by resource, time horizon or command type

Response Planning

• Exploit overlapping resource capabilities

• Generate, evaluate & select response plans

Control

• Exploit independent resource capabilities

• Use assignments w/ performance parameters to compute control

Resources

Mgmt Nodes

DF/RM Duality Allows Similar Approaches & Consistent Operation

DATA FUSION

Fusion Architecture

• “Fan-in” Tree

• Data batching by source, past time or data type

Association

• Exploit overlappingmeasurement observables

• Generate, evaluate & select association hypotheses

Estimation

• Exploit independent measurement observables

• Use associations w/ a prioriparameters to compute estimates

Sensors

Fusion Nodes

DUALITY

DF & RM Node Duality FacilitatesUnderstanding of Alternatives & Reuse

DATA ASSOCIATION

DATA FUSION NODE

DATAPREPARATION

(CommonReferencing)

RESOURCE MGT CONTROLS & DF NEEDS

Sources& Prior

DF Nodes

Useror Next

DF NodeSTATE

ESTIMATION&

PREDICTION

HYPOTHESISGENERATION

HYPOTHESISEVALUATION

HYPOTHESISSELECTION

PLANTASKING/CONTROL

RESOURCE MANAGEMENT NODE

TASKPREPARATION

(CommonReferencing)

RESOURCE STATUS

RESPONSE TASK PLANNING

PLANGENERATION

PLANEVALUATION

Useror Prior

RM Node

Resources& Next

RM Nodes

PLANSELECTION

SENSOR STATUS

RM NEEDS & DATA FUSION ESTIMATES

Sample Interlaced Network of DF&RM Dual Level Interactions

Source

Fusion

Level 0:

Feature

Assessment

Sensor

Sensor

Sensors

Source

Fusion

Level 1:

Entity

Assessment

Fusion

Level 2:

Situation

Assessment

Fusion

Level 3:

Impact

Assessment

Management

Level 0:

Resource

Signal

Management

Management

Level 1:

Resource

Response

Management

Weapons

Mission

Management

Level 3:

Mission

Objective

Management

Management

Level 2:

Resource

Relationship

ManagementComm

Data Fusion Node Network

Resource Management Node Network

Resources

DF&RM

Reactive

Loop

DF&RM

Reactive

Loop

DF&RM

Reactive

Loop

DF&RM

Reactive

Loop

Sources

Fusion

Level 4:

System

Assessment

Management

Level 4:

System

Management

DF&RM

Reactive

Loop

User

I/O(at all

levels

as

needed)

Sample DF&RM Node Network for Battlefield Awareness

HSI

EO/IR

m

s,d

Tracks

s,d

Impacts• Mission

Objectives

• Vulnerabilities

• Cost

Entities• Terrorists

• ClandestineLabs

• Vehicles

• Underground

facilities

• - etc.

Relationships• Terrorist Nets

• Red Units

• Joint Force

• Red-to-Blue

• Logistics

Tracks

s,d

L.1

s,dTracks

s,d

F

L.1

Tracks

s,d

Tracks

s,d

s,d

mm,s,

d s,d

F

M

F

M

mLF-

RADAR

mF

M

F

M

F

M

mSAR

mF

M

F

M

F

M

F

M

HUMINT

KEY

m = Measurements (pixels, waveforms, etc.)

s = States (continuous parameters)

d = Discrete Attributes (target type, IFFN)

r = Aggregate Mission Response Directives

t = Resource tasks, Resource modes

c = Command/Control

= Data Fusion Node = Resource Mgmt NodeF M

t t

ttc

ttc

tc

tc

L.3

dF

M

L.2

dF

Mr

L.1

dF

Mr,t

Sample System

Architecture

U

S

E

R

r

Sample Interlaced Tree of DF&RM Nodes

UserInterface*

Theater

Fusion/BMC4I

Platform Fusion

FN

MN

MN

MN MN

FN

FN

On/Off Board

Fusion

FN

MN

Internetted Sources

MN

FN

MN

FN FN

FN = Fusion Node

MN = Management

Node

MN

FN

MN

FN

H

A

R

D

W

A

R

E

MASINT

Radar

ESM

Theater Sites

MN

FN

MN

FN

Platform Sensors

MN

FN

Nat’l Asset Fusion

MN

FN

Theater Platforms

MN

FN

Theater

Platform Fusion

MN

FN

MN

FN

Platform

Nat’l Asset Platforms

Sensors

Weapons

FLIR

Sensors

Theater Sources

*User Interfaces (such as

shown) Can Occur

Throughout DF&RM

Tree/Network

MN MN

FN

MN MN

FN

Sensor Fusion

RF Fusion

Other Information/Response Requests

The DNN Architecture DF&RM System Engineering Process Includes Rapid Prototyping

Operational Test & Evaluation

DESIGN PHASE

1. Operational

Architecture

Design:

System Role

2. System

Architecture Design:

Fusion &

Management

Network

3. Component

Function Design:

Fusion &

Management Node

Processing

4. Detailed Design/

Development:

Pattern Application

Functional

Partitioning

Point

Design

Functional

Partitioning

Point

Design

Functional

Partitioning

Point

Design

Functional

Partitioning

Point

Design

PerformanceEvaluation

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Feedback for System Role Optimization

Feedback for Network Optimization

Feedback for Node Optimization

Feedback for Design (Pattern)

Optimization

1

2

3

4

Design

Constraints

User Needs

& Use Cases




Design Development (per level)

AGENDA

➢DF&RM Dual Node Network (DNN) Technical Architecture

❖Distributed Data Fusion Node Networks

➢Data Association Hypothesis Evaluation Alternatives

The DNN Architecture DF&RM System Engineering Process


DESIGN PHASE

1. Operational

Architecture

Design:

System Role

2. System


Fusion &

Management

Network

3. Component

Function Design:

Fusion &

Management Node

Processing

4. Detailed Design/

Development:

Pattern Application

Functional

Partitioning

Point

Design

Functional

Partitioning

Point

Design

Functional

Partitioning

Point

Design

Functional

Partitioning

Point

Design


Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis





Optimization

1

2

3

4

Design

Constraints

User Needs

& Scenarios





Fusion Node Network Examples

FN FN FN FN FN FN FN

Radar

Single Source Tracker

Time-Batched

Radar

ESMImaging

IR


t1 t2 t3 t4 t5 t6 t7

Event-Driven

Radar

ESMImaging

IR

FN FN FN FN FN FN


Radar

ESM

FN FN FN

FN FN

Event-DrivenFusion

Event-DrivenFusion

Time-BatchedFusion

FN

Hybrid Tree

Global Track Reinitialization with Local Track Sharing

site 2 multi-sensor fusion nodes

site 2 sensor fusion nodes




MULTI-SENSOR FUSION WITH BATCHED LOCAL TRACK SHARING


Complementarity of Five Distributed Tracking Fusion Networks

FUSION TREES PERFORMANCE DESCRIPTORS

Multi-Sensor Fusion with

Data Communicated

Track Reinitialization

Sensor Tracker Impacts

Track Error Correlations

Flexibility Issues

Measurement Sharing

sensor measurements

no reinitialization

sensor track tailoring at global sites

none highest bandwidth comm

Sensor Node Track Reinitialization

local track state

sensor filter reinitialization after send

sensor filter modifications needed

sensor filter process noise correlations

simultaneous comm output

Tracklets From Tracks

local track state

no reinitialization

only standard KF updates

inverse KF to remove correlations

For non-maneuvering entities

Local Track Sharing

local track state (plus reports)

global track reinitialization

use tailored sensor trackers as is

process noise & misalignments correlate

Sending reports Increases BW

Globlal Track Sharing

global track state

no global filter use tailored sensor trackers as is

correlations reduce accuracy

ID with pedigree fused

AOC

JSFFlight Leader

JSFIndividual

Sensors

low-latency

information path

high-latency

information path

intermediate-latency

information path

Fusion Updates CTP with New Data; Adjudication Maintains Consistency

Note: Alerts of sufficient

confidence and priority

propagate immediately to

all affected nodes at all

levels

External Systems

Off-boardassets

CTP

CTP

CTP

Updates

Off-boardassets

Updates

UpdatesExternalassets

Adjudication

Sensor Reports

Sensor

Tracks

Sensor Tracks

Sensor

Fusion

Adjudication

Advisements Sent up and Directives Sent Down Echelons Insure Consistency of the Operational Picture.

AGENDA

➢DF&RM Dual Node Network (DNN) Technical Architecture

➢Distributed Data Fusion Node Networks

❖Data Association Hypothesis Evaluation Alternatives

The DNN Architecture DF&RM System Engineering Process


DESIGN PHASE

1. Operational

Architecture

Design:

System Role

2. System


Fusion &

Management

Network

3. Component

Function Design:

Fusion &

Management Node

Processing

4. Detailed Design/

Development:

Pattern Application

Functional

Partitioning

Point

Design

Functional

Partitioning

Point

Design

Functional

Partitioning

Point

Design

Functional

Partitioning

Point

Design


Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis

Requirements

Analysis





Optimization

1

2

3

4

Design

Constraints

User Needs

& Scenarios





Data Association Problems Occur at All Fusion Levels

0.20 0.01 0.44 0.30 0.02 0.20

0.02 0.16 0.11 0.01 0.56 0.15

0.03 0.02 0.06 0.87 0.01 0.01

0.07 0.19 0.03 0.17 0.31 0.23

0.11 0.01 0.46 0.12 0.15 0.16

0.67 0.02 0.01 0.20 0.09 0.01

0.20 0.01 0.44 0.30 0.02 0.20

0.02 0.16 0.11 0.01 0.56 0.15

0.03 0.02 0.06 0.87 0.01 0.01

0.07 0.19 0.03 0.17 0.31 0.23

0.11 0.01 0.46 0.12 0.15 0.16

0.67 0.02 0.01 0.20 0.09 0.01

0.20 0.01 0.44 0.30 0.02 0.20

0.02 0.16 0.11 0.01 0.56 0.15

0.03 0.02 0.06 0.87 0.01 0.01

0.07 0.19 0.03 0.17 0.31 0.23

0.11 0.01 0.46 0.12 0.15 0.16

0.67 0.02 0.01 0.20 0.09 0.01

0.20 0.01 0.44 0.30 0.02 0.20

0.02 0.16 0.11 0.01 0.56 0.15

0.03 0.02 0.06 0.87 0.01 0.01

0.07 0.19 0.03 0.17 0.31 0.23

0.11 0.01 0.46 0.12 0.15 0.16

0.67 0.02 0.01 0.20 0.09 0.01

Entity Features/Signals

Featu

res

Measu

rem

en

ts/

Level 0Feature/SignalAssessment

Scores

Plans/COAs

En

titi

es/

Rela

tio

nsh

ips

Level 3Impact

Assessment

Scores

Truth/Desired

DF

&R

M

Ou

tpu

tsLevel 4System

Assessment

Scores

Entity TracksE

nti

ty

Rep

ort

s/T

racks 0.20 0.01 0.44 0.30 0.02 0.20

0.02 0.16 0.11 0.01 0.56 0.15

0.03 0.02 0.06 0.87 0.01 0.01

0.07 0.19 0.03 0.17 0.31 0.23

0.11 0.01 0.46 0.12 0.15 0.16

0.67 0.02 0.01 0.20 0.09 0.01

Level 1Entity

Assessment

ScoresLevel 2

SituationAssessment

Entity Aggregations/Relationships

En

titi

es/

Rela

tio

nsh

ips

Scores

(2-D AssignmentMatrix

Examples)

Resource Management Has Dual Response Planning Problems at All Levels

0.20 0.01 0.44 0.30 0.02 0.20

0.02 0.16 0.11 0.01 0.56 0.15

0.03 0.02 0.06 0.87 0.01 0.01

0.07 0.19 0.03 0.17 0.31 0.23

0.11 0.01 0.46 0.12 0.15 0.16

0.67 0.02 0.01 0.20 0.09 0.01

0.20 0.01 0.44 0.30 0.02 0.20

0.02 0.16 0.11 0.01 0.56 0.15

0.03 0.02 0.06 0.87 0.01 0.01

0.07 0.19 0.03 0.17 0.31 0.23

0.11 0.01 0.46 0.12 0.15 0.16

0.67 0.02 0.01 0.20 0.09 0.01

0.20 0.01 0.44 0.30 0.02 0.20

0.02 0.16 0.11 0.01 0.56 0.15

0.03 0.02 0.06 0.87 0.01 0.01

0.07 0.19 0.03 0.17 0.31 0.23

0.11 0.01 0.46 0.12 0.15 0.16

0.67 0.02 0.01 0.20 0.09 0.01

0.20 0.01 0.44 0.30 0.02 0.20

0.02 0.16 0.11 0.01 0.56 0.15

0.03 0.02 0.06 0.87 0.01 0.01

0.07 0.19 0.03 0.17 0.31 0.23

0.11 0.01 0.46 0.12 0.15 0.16

0.67 0.02 0.01 0.20 0.09 0.01

Resource Signals

Tasks/S

ign

als

Level 0Resource

SignalManagement

Scores

Objectives

Reso

urc

es/

Rela

tio

nsh

ips

Level 3Mission

ObjectiveManagement

Scores

DF&RM Designs

DF

&R

MF

un

cti

on

s

Level 4System

Management

Scores

Resource Responses/ModesTasks

0.20 0.01 0.44 0.30 0.02 0.20

0.02 0.16 0.11 0.01 0.56 0.15

0.03 0.02 0.06 0.87 0.01 0.01

0.07 0.19 0.03 0.17 0.31 0.23

0.11 0.01 0.46 0.12 0.15 0.16

0.67 0.02 0.01 0.20 0.09 0.01

Level 1ResourceResponse

Management

Scores

Level 2Resource

RelationshipManagement

Resource Aggregations/

Relationships

Reso

urc

es/

Rela

tio

nsh

ips

Scores

(2-D AssignmentMatrix

Examples)

Data Association Is the Core of Data Fusion

• DETECT AND RESOLVE

DATA CONFLICTS

• CONVERT DATA TO

COMMON TIME AND

COORDINATE FRAME

• COMPENSATE FOR

SOURCE

MISALIGNMENT

• NORMALIZE

CONFIDENCE

• ESTIMATE/PREDICT

ENTITY STATES

- KINEMATICS, ATTRIBUTES,

ID, RELATIONAL STATES

• ESTIMATE SENSOR/SOURCE

MISALIGNMENTS

• FEED FORWARD SOURCE/

SENSOR STATUS

• GENERATE FEASIBLE &

CONFIRMED ASSOCIATION

HYPOTHESES

• SCORE HYPOTHESIZED

DATA ASSOCIATIONS

• SELECT, DELETE, OR

FEEDBACK DATA

ASSOCIATIONS

USEROR NEXTFUSIONNODE

STATEESTIMATION

& PREDICTION

DATA ASSOCIATION

DATA FUSION NODE



HYPOTHESISSELECTION

DATAALIGNMENT

(CommonReferencing)

PRIORDATA FUSION

NODES &SOURCES

RESOURCE MGT CONTROLSSOURCE SENSOR STATUS

Level 1 Entity Data Association Is a Labeled Set Covering Problem

Hypothesis

(Track) X2

Sensor Reports yi(mean & covariance)

Hypothesis

(Track) X1y1

y5

y4

y2

y3

Hypothesis

(Track) X2

Sensor Reports yi(mean & covariance)

Hypothesis

(Track) X1y1

y5(poor resolution)

y4

y2

y3

Set Covering Set Partitioning

State est. (Partitioning)

State est. (Covering)

y6Unassociated

y6Unassociated

State est.

Hypothesis Evaluation Is the Core of Data Association

• DETECT AND RESOLVE

DATA CONFLICTS

• CONVERT DATA TO

COMMON TIME AND

COORDINATE FRAME

• COMPENSATE FOR

SOURCE

MISALIGNMENT

• NORMALIZE

CONFIDENCE


ENTITY STATES



TRACK CONFIDENCES


MISALIGNMENTS


SENSOR STATUS



HYPOTHESES


DATA ASSOCIATIONS



TRACK CONFIDENCES


FEEDBACK DATA

ASSOCIATIONS


STATEESTIMATION

& PREDICTION

DATA ASSOCIATION

DATA FUSION NODE



HYPOTHESISSELECTION

DATAALIGNMENT

(CommonReferencing)

PRIORDATA FUSION

NODES &SOURCES


Processing Load Is Balanced Within Each Fusion Node Component

( ) − LOGP REPORTSj | 0

Prior Data Fusion

Nodes &Sources

DataPreparation(Common

Referencing)

HypothesisGeneration

HypothesisEvaluation

HypothesisSelection

State

Estimation&

Prediction

Data Correlation

Data Fusion Tree Node

User or

Next FusionNode

RAW SENSOR DATA FEASIBLE HYPOTHESIS ASSOCIATION SCORING SEARCH ALGORITHMS

REPORT ASSOCIATION

MATRIX

t = 1 t = 2

t = 3 t = 4

12 Sensor

Reports

Data

Point

#1

Feasible

Track #6

10

1

2

3

4

8

11

5

12

97

6

10

DETERMINISTIC DATA ASSOCIATION THEN TARGET STATE ESTIMATION

MAX P(H|REPORTS) = MAX[P(REPORTS|H)P(H) THEN MAX P( H)

H H

TARGET STATE ESTIMATION WITH PROBABILISTIC DATA ASSOCIATION

MAX P(H|REPORTS) = MAX[ P(REPORTS|H)P(H, )P(H|)P()

H

JOINT ASSOCIATION DECISION AND TARGET STATE ESTIMATION

MAX P(H, |REPORTS) = MAX[MAX P( |REPORTS,H)P(H) P(H|REPORTS)

H,

H

~

Chi-Square Tail Test:

Fix P(d2|H1) = then Test for Rejection of H1: Mean of (X-Y) = 0

-2 ln ( )

X s dsnC

2

( )

>

<

d1

d2

where C = (X-Y) V (X-Y)T -1

Area Tail =

PXj jj J

MINIMIZE

X

WHERE A Xij jj J

1

Pj =

Pierce Column - Search (BinaryTree)

X4

X1 = 1

X2 = 1

X3 = 1

X1 = 0

X2 = 0

X3 = 0

X2 = 1

X2 = 0

X3 = 0

Hypothesis#1

Hypothesis#2

ActualPositions

Prior Data Fusion

Nodes &Sources

DataPreparation(Common

Referencing)

HypothesisGeneration

HypothesisEvaluation

HypothesisSelection

State

Estimation&

Prediction

Data Correlation

Data Fusion Tree Node

User or

Next FusionNode

RAW SENSOR DATA FEASIBLE HYPOTHESIS ASSOCIATION SCORING SEARCH ALGORITHMS

REPORT ASSOCIATION

MATRIX

t = 1 t = 2

t = 3 t = 4

12 Sensor

Reports

Data

Point

#1

Feasible

Track #6

10

1

2

3

4

8

11

5

12

97

6

10

DETERMINISTIC DATA ASSOCIATION THEN TARGET STATE ESTIMATION

MAX P(H|REPORTS) = MAX[P(REPORTS|H)P(H) THEN MAX P( H)

H H

TARGET STATE ESTIMATION WITH PROBABILISTIC DATA ASSOCIATION

MAX P(H|REPORTS) = MAX[ P(REPORTS|H)P(H, )P(H|)P()

H

JOINT ASSOCIATION DECISION AND TARGET STATE ESTIMATION

MAX P(H, |REPORTS) = MAX[MAX P( |REPORTS,H)P(H) P(H|REPORTS)

H,

H

~

Chi-Square Tail Test:

Fix P(d2|H1) = then Test for Rejection of H1: Mean of (X-Y) = 0

-2 ln ( )

X s dsnC

2

( )

>

<

d1

d2

where C = (X-Y) V (X-Y)T -1

Area Tail =

PXj jj J

MINIMIZE

X

WHERE A Xij jj J

1

Pj =

Pierce Column - Search (BinaryTree)

X4

X1 = 1

X2 = 1

X3 = 1

X1 = 0

X2 = 0

X3 = 0

X2 = 1

X2 = 0

X3 = 0

Hypothesis#1

Hypothesis#2

ActualPositions

Applicability of Alternative Scoring Schemes (1 of 2)

• Probabilistic: Preferred if statistics known

> Chi-Square Distance

– Doesn’t require prior densities

– Useful for comparing multi-dimensional Gaussian data

– However, no natural way to incorporate attribute and a priori data

> Max Likelihood

– Doesn’t require unconditional prior densities, p(x)

– Does require conditional priors, p(Z|x)

> Bayesian Maximum a Posteriori (MAP)

– Naturally combines kinematics, attribute, and a priori data

– Provides natural track association confidence measure

– However, requires prior probability (e.g. kinematics and class) densities; difficult to specify

Applicability of Alternative Scoring Schemes (2 of 2)

• Non-Probabilistic: Useful if high uncertainty in the uncertainty

> Evidential (Dempster-Shafer)

– Non-statistical: User specifies evidence “mass” values (support and plausibility numbers)

– Essentially 2-point calculus (uniform uncertainty-in-the-uncertainty with simple knowledge combination rules)

> Fuzzy Sets

– User specifies membership functions to represent the uncertainty-in-the-uncertainty

– User specifies fuzzy knowledge combination rules (e.g., sum, prod, max/min) which are much easier compute than second-order Bayesian

– More complex to develop, maintain, and extend

> Confidence Factors and Other ad hoc Methods

– Explicit derivation of logical relationships

– Generally ad hoc weightings to relate significance of factors

– Can include information theoretic and utility weightings

• Chi-square (Mahalanobis) Scoring:

• ItV-1I=[R1-T2]2/[σR

2+σT22] = 22/[1+1]=2

• ItV-1I=[R1-T3]2/[σR

2+σT32]=42/[1+16]=16/17

• R associated to 1 sigma away but further distance away less accurate T3

• Max. a Posteriori (Bayesian):

• [2πV]-.5 e(-.5ItV-1I) =[6.28*2]-.5

e(-.5[R1-T2]2/[σR

2+σT22]) ~ .28 e-1 ~ .10

• [2πV]-.5 e(-.5ItV-1I) =[6.28*17]-.5 e(-.5[R1-T3]

2/[σR2+σT3

2]) ~ .097 e-.47 ~ .060

• R is associated to the closer more accurate T2

MAP Scoring Correctly Balances Less Accurate Further Away Tracks

R1 =0 T2=2 T3=4

T3 Selected

R1 =0 T2=2 T3=4

2σ2~1σ2

.10 .06

T2 SelectedInput Report

Alternative MAP Association Scoring

•Deterministic Data Association then target estimation

MAX P H REPORTS MAX P REPORTS H P H THEN MAX P HH H

( | ) [ ( | ) ( ) ( | )^

= qq

•

MAX P REPORTS MAX P REPORTS H P H P

H

( | ) ( | , ) ( | ) ( )q q q qq q

=

•

MAX P H REPORTS MAX MAX P REPORTS H P H REPORTS

H H

( , | ) ( | , ) ( | )

,

q q

q q

=

H is the association hypothesis and Theta is the track state.

Target state estimation with probabilistic data association

Joint association decision and target state estimation

• The total scene hypothesis score is the product of the individual hypothesis

scores for the 5 possible hypothesis types: • association hypotheses

• pop-up (i.e., track initiation) hypotheses

• input false alarm (FA) hypotheses

• track propagation (missed coverage) hypotheses

• drop track (false track) hypotheses

• Pd and Pfa use track association confidences and incorporate the entity birth and

death statistics

• Track confidence estimates are needed to differentiate the 5 hypotheses types

• When the class tree uncertainty-in-the-uncertainty is high it is not used in scoring

Max A Posteriori (MAP) Hypothesis Scoring

Source Noncommensurate Attributes Scored Using Entity Class Tree

• Sources 1 & 2 have noncommensurate attributes if for an exhaustive set of

disjoint of entity classes, K,

P(Z(S1) | Z(S2), Class K, Y(Si ), H) = P(Z(S1) | Class K, Y(S1 ), H)

where,

• Z(Si) is the set of measured attributes (i.e., all non kinematics measurements) from each

source i,

• H is the association hypothesis between sources S1 & S2,

• Y(Si) are the measured kinematics from the two sources

• All source attributes not conditionally independent are treated as separately

commensurate parameters

• For commensurate sources, feature differences are scored

Sample Hierarchical Disjoint Entity Class Taxonomy

T72

(A,M)

M60

(A,M)BMP

(A,M)

Helicopter (M)- Manually classified: 1.0

Unknown (A,M)- includes personnel

-includes helicopters if

automatically detected

FV432

(A,M)

Spartan

(A,M)

HMMWV

(A,M)

M1A2

(A,M)

Challenger

(M)Warrior

(M)

Range Rover

(M)

BMP: 50.4

T72: 5.6

M60: 1.6

M1: 0

FV432: 10.4

Spartan: 10.4

HMMWV: 1.6

Unknown: 20

T72: 36

M60: 1.6

M1: 1.6

BMP: 20

FV432: 10.4

Spartan: 10.4

HMMWV: 0

Unknown: 20

M60: 53.6

T72: 0

M1: 5.6

BMP: 0

FV432: 10.4

Spartan: 10.4

HMMWV: 0

Unknown:20

M1: 53.6

T72: 1.6

M60: 2.4

BMP: 0

FV432: 11.2

Spartan: 11.2

HMMWV: 0

Unknown: 20

FV432: 41.6

T72: 6.4

M60: 6.4

M1: 6.4

BMP: 6.4

Spartan: 6.4

HMMWV: 6.4

Unknown: 20

Spartan: 41.6

T72: 6.4

M60: 6.4

M1: 6.4

BMP: 6.4

FV432: 6.4

HMMWV: 6.4

Unknown: 20

HMMWV:40

T72: 1.6

M60: 4.8

M1: 0

BMP: 16

FV432: 8.8

Spartan: 8.8

Unknown: 20

Note: Values based on

simulation test results

except for FV432, Spartan,

and Unknown which were

added in based on

engineering judgment.

Tracked Vehicle (A)

Wheeled Vehicle (A)

Unclassified (A)-No ATR

Entity Type Declarations (Statistics per Truth Type)

T72

(A,M)

M60

(A,M)BMP

(A,M)

Helicopter (M)- Manually classified: 1.0

Unknown (A,M)- includes personnel

-includes helicopters if

automatically detected

FV432

(A,M)

Spartan

(A,M)

HMMWV

(A,M)

M1A2

(A,M)

Challenger

(M)Warrior

(M)

Range Rover

(M)

BMP: 50.4

T72: 5.6

M60: 1.6

M1: 0

FV432: 10.4

Spartan: 10.4

HMMWV: 1.6

Unknown: 20

T72: 36

M60: 1.6

M1: 1.6

BMP: 20

FV432: 10.4

Spartan: 10.4

HMMWV: 0

Unknown: 20

M60: 53.6

T72: 0

M1: 5.6

BMP: 0

FV432: 10.4

Spartan: 10.4

HMMWV: 0

Unknown:20

M1: 53.6

T72: 1.6

M60: 2.4

BMP: 0

FV432: 11.2

Spartan: 11.2

HMMWV: 0

Unknown: 20

FV432: 41.6

T72: 6.4

M60: 6.4

M1: 6.4

BMP: 6.4

Spartan: 6.4

HMMWV: 6.4

Unknown: 20

Spartan: 41.6

T72: 6.4

M60: 6.4

M1: 6.4

BMP: 6.4

FV432: 6.4

HMMWV: 6.4

Unknown: 20

HMMWV:40

T72: 1.6

M60: 4.8

M1: 0

BMP: 16

FV432: 8.8

Spartan: 8.8

Unknown: 20

Note: Values based on

simulation test results

except for FV432, Spartan,

and Unknown which were

added in based on

engineering judgment.

Tracked Vehicle (A)

Wheeled Vehicle (A)

Unclassified (A)-No ATR

Entity Type Declarations (Statistics per Truth Type)

A: automated

M: manual

Max a Posteriori Association Hypothesis Scoring

The total scene hypothesis score is the product of scores for 5 types of S to T association

hypotheses of kinematics, Y, attributes, Z, and entity class confidences, K:

1. Association Hypotheses

P(Y(S)|Y(T),H) P(Z(S), Z(T)|Y(S), Y(T), H) P(H) = {|V|-1/2 } exp[-1/2{IT V-1 I }]

• {K[P(K|Z(T),Y(T), H) P(K|Z(S),Y(S), H)/P(K|Y(T),Y(S), H)]} • [1-PFA (S)] [1- PFA(T)] PD (S) PD (T)

2. Pop-up (i.e., Track Initiation) Hypotheses

P(Y(S)|Y(T),H) P(Z(S), Z(T)|Y(S), Y(T), H) P(H) = {E(|V|-1/2 )} exp[-1/2{}] • [1-PFA (S)] [1- PD(T)] PD (S)

3. False Alarm (FA) Hypotheses

P(Y(S)|Y(T),H) P(Z(S), Z(T)|Y(S), Y(T), H) P(H) = { E(|V|-1/2 )} exp[-1/2{}] • PFA (S) PD (S)

4. Propagation Hypotheses

P(Y(S)|Y(T),H) P(Z(S), Z(T)|Y(S), Y(T), H) P(H) = [1-PFA (T)] [1- PD(S)] PD (T) 5. Track Drop Hypotheses

P(Y(S)|Y(T),H) P(Z(S), Z(T)|Y(S), Y(T), H) P(H) = PFA (T) PD (T)

Scoring Nomenclature

• Y(S) are the sensor report Gaussian kinematics with covariance R

• Y(T) are the track Gaussian kinematics with covariance P +k ,

• H is one of 5 association hypothesis types, E is expectation fcn

• |V| is determinant of innovations covariance, V = H [P +k] HT + R,

• is the mean of the chi-square statistic (i.e., {IT V-1 I })

• I is the innovations vector, I = Y(S) - H Y(T),

• P(K) are the confidences of the disjoint entity class tree,

• Z(T) [Z(S)] are the parameters/attributes from the track [report],

• PD(S) [PD(T)] is the sensor [track file] probability of detection

• PFA (S) [PFA (T)] is the sensor [track file] probability of false alarm,

Approximate Class Confidence Generation from Declarations

• P(K|D) = P(D|K) P(K) / ΣT(P(Truth T) P(D |Truth T) where

• P(K|D) is the probability of the entity being of class K given the specified sensor declaration D that is computed

for all the possible disjoint classes. These terms are inserted for the n P(K|Z(S),Y(S), H) sensor report disjoint

classification type confidences.

• P(K) is the a priori probability of the entity being of class K

• P(D|K) is the probability that the declaration D is made given the entity is of class K from the declaration

confusion matrix

• P(D |Truth T) is the probability of the specified declaration D given the entity is of truth type T from the

declaration confusion matrix where T varies over the possible scenario truths

• P(Truth T) is the a priori probability of the truth in the scenario being of type T where T varies over the possible

scenario truths

MAP Scoring Uses Kinematics, ID, Track Confidence & Pedigree Information

• Uses the separation point on the PDF as the kinematics score, so high uncertainty tracks do not overly attract reports as w/chi-square scoring

• Bayesian scoring and update of the classification uncertainties with pedigree of noncommensurates used for class error correlation compensation or separate noncommensurate class vectors

• Track confidence estimation provides rigorous basis for the scoring of the four non-association hypotheses

• Misalignment bias states & uncertainties added for scoring and to remove relative misalignments

Track Confidence Is Updated Using Source Parameters & Association Results

• DETECT AND

RESOLVE DATA

CONFLICTS

• CONVERT TRACK

CONFIDENCE & DATA

TO COMMON TIME

AND COORDINATE

FRAME

• COMPENSATE FOR

SOURCE

MISALIGNMENT


ENTITY STATES




MISALIGNMENTS

• ESTIMATE TRACK CONFIDENCES


SENSOR STATUS



HYPOTHESES


DATA ASSOCIATIONS USING

TRACK CONFIDENCE


FEEDBACK DATA

ASSOCIATIONS


STATEESTIMATION

& PREDICTION

DATA ASSOCIATION

DATA FUSION NODE



HYPOTHESISSELECTION

DATAALIGNMENT

(CommonReferencing)

PRIORDATA FUSION

NODES &SOURCES


Track Confidences Needed for MAP: Bayesian Equations for COP Track Confidence Have Been Derived

• Propagation of Probability for Entity Track in Consistent Operational

Picture (COP) & False Track

• Track Confidence Contribution to Association Hypothesis Scoring

• Update of Probability of Entity Track in COP and False Track

Confidences With Track Propagations and Pop Ups

• Update of COP Probability of False Track for Associated Tracks,

Propagated Tracks, & Pop Ups

Hyp Eval Problem to Solution Space Mapping

UnifiedNeuralLogic/SymbolicPossibi-listicProbabilisticSOLUTION SPACE

Y

N

Y

Y

Y

N

Y

Y

Y

Y

Y

Y

Y

RS

Y

Y

Y

Y

Y

Y

N

Y

Y

Y

N

Y

Rec

Y

Y

Y

Y

N

Y

N

Y

Y

Y

N

C-B

Y

Y

Y

Y

N

Y

N

Y

Y

Y

N

Y

US

Y

Y

Y

Y

Y

Y

N

Y

Y

Y

N

Y

FF

N

N

N

Y

Y

Y

Y

Y

AdH

YYY• Result explanation

Y

Y

Y

Y

N

Y

N

Y

Y

N

Chi

YYYYY• Numerical scores

YYY• Discrete score bins

NNNNNNYYN• Error PDF known

Y• Differing conditionals

• Differing dimensions

Y• Partial data

YYY• Non-parametric data

YN• A priori sensor data

YY• Parameter attributes

YY• Kinematics

YY• Identity/attributes

YYNYyNN• High processing Avail

• Robustness to error

YYYYYYYYYY• Self-coded/trained

• Training set required

YY• User adaptability

NNNYYYNYY• Score accuracy

NN• Compute efficiency

NNNNNNNYYY• Low cost/complexity

PERFORM MEAS

Y• Confidence function

Y• Multi scores per

• Yes/no, pass-through

SCORE OUTPUT

• Unknown structure

YY• High uncertainty

Y• Spatio-temporal

YYYY• Linguistic data

INPUT DATA

ESSDS/FFuzDSInfCEANPBayLklPROBLEM SPACE

KEY

AdH Ad Hoc

Lkl Likelihood

Bay Bayesian

NP Non-parametric

Chi Chi-Squared

CEA Conditioned Event

Algebra

Inf Information Theoretic

DS Dempster-Shafer

Fuz Fuzzy Logic

S/F Scripts/ Frames

SD Semantic Distance

ES Expert Systems

C-B Case-Based Reasoning

US Unsupervised Learning

FF Feed-Forward

Rec Recurrent Supervised

Learning

RS Random Set

Alternative DF&RM Techniques Are Synergistic

Methods Approach EventRepresentation

ProblemDomain

SolutionDevelopment

Costs/Risks Performance Verification Speed

Ad Hoc analyst

driven

table look-up predefined

fixed

features

rule-of-thumb simple not

upgradable

approximate;

brittle

all cases

tested

fast table look-up

Probabilistic algorithm

driven

pointwise

probability

rigorously

defined

features

analyst solves

rigorously

upgradable

SW

precise;

extendable

alternative path

tests

via path

parallelization

Possibilistic algorithm

driven

uncertainty-in-

the-uncertainty

feature

uncertainties

known

analyst solves

approximation

upgradable;

more

complex

Broader app’s;

extendable

alternative path

tests

via path

parallelization

Logic/

Symbolic

rule driven setwise degree

of membership

expert

described

features

expert defines

rules

rule

compatibility/

scalability

gets close;

user adaptable

rules explanation via rule

parallelization

Neural

Networks

self-

organized

firing level

patterns

unknown

feature

relationships

data driven;

user objectives

training

breadth; HW

scalability

approximate;

non-linear

interpolation

numerous training

cases

massively parallel

chips

Unified algorithm

& rule

driven

normalized

representation

combination

s of the

above

analyst solves

hybrid

most

complex

most breadth alternative path

tests

via approximation

Decision Flow for Technique Selection (Hypothesis Evaluation Example)

SCRIPTS/FRAMES

SYSTEM RULES

HYBRIDS

(5)

YES CASE-BASED REASONINGIS ON-LINE LEARNING

FROM USER NEEDED?

NO

Logical, Symbolic and ad hoc

Hypothesis Evaluation Technique SelectionPossibilistic, Non-Parametric and other RigorousHypothesis Evaluation Technique Selection

(3)

CONDITIONAL EVENT ALGEBRAYES

IS SCORINGOF INFORMATION

CONDITIONED UPONMULTIPLE EVENTS

YES

YES

EVIDENTIAL

FUZZYSET THEORY

YES

IS UNIFORMDISTRIBUTIONSUFFICIENT?

YES

NO

NO

IS A CONSISTENT MATHEMATICAL BASIS FOR SCORING NEEDED?

IS HIGH THROUGHPUT PER WATT SELF-CODING PATTERN RECOGNITION NEEDED?

IS JOINT PROBABILITY DENSITY FUNCTION SUFFICIENTLY KNOWN?

NO

POSSIBLISTIC & NON-PARAMETRIC

PROBABILISTIC

LOGIC/SYMBOLIC & AD HOC

NEURAL NETWORKS

YES

YES

(2)

(3)

(4)

(5)

(2)

[92]

HE Technique SelectionDecision Flow (4 of 5)[4,17,19]

Neural NetworkHypothesis Evaluation Technique Selection

UNSUPERVISED CLUSTERING NN

NO

YES

ARE SUFFICIENT SCORING TRAININGSETS AVAILABLE? RECURRENT SUPERVISED NNYES

IS SPATIO-TEMPORALPATTERN RECOGNITION

NEEDED?

FEED-FORWARD SUPERVISED NNNO

(4)

Solution Selection Depends upon Problem Difficulty

Pe

rfo

rma

nce

(lo

g s

cale

)

Problem Difficulty (log scale)

Intelligent

Static

Data-Driven

Systems

Expert

Model-Driven

Systems

Known Modeled

Behavior

Intelligent

Adaptive

Goal-Driven

Systems

Consistent Historically

-Based Behavior

Inconsistent [Not Based

Upon Historical] Behavior

Hypothesis Selection Determines How Alternative Association World Views to Be Maintained

• DETECT AND

RESOLVE DATA

CONFLICTS

• CONVERT TRACK

CONFIDENCE & DATA

TO COMMON TIME

AND COORDINATE

FRAME

• COMPENSATE FOR

SOURCE

MISALIGNMENT


ENTITY STATES




MISALIGNMENTS

• ESTIMATE TRACK CONFIDENCES


SENSOR STATUS



HYPOTHESES


DATA ASSOCIATIONS USING

TRACK CONFIDENCE


FEEDBACK DATA

ASSOCIATIONS


STATEESTIMATION

& PREDICTION

DATA ASSOCIATION

DATA FUSION NODE



HYPOTHESISSELECTION

DATAALIGNMENT

(CommonReferencing)

PRIORDATA FUSION

NODES &SOURCES


Hypothesis Selection Problem

• Need to Search through Association Matrix to find best Global Hypothesis

• Association Matrix:

• Types of Global Hypotheses• Set Partitioning: no two tracks (local hypotheses) share a report

• Set Covering: There may be shared reports

• N-D Approaches: Search All Scans by All Sources

• Globally Optimal Solution

• Computationally Demanding (NP-Hard: Exponential Run-Time)

• 2-D Approaches: Search only Current Scan

• Locally Optimal Solution

• Polynomial Run-Time

Reports

Tra

cks

Scores

Set Partitioning

Set Covering

TYPES OF GLOBAL HYPOTHESIS

• “No Observation” columns added to denote the better hypothesis, H2, of false or propagated tracks for unassociated tracks

• “No Association” rows added to denote the better hypothesis, H1, of false alarm or initiated tracks for unassociated reports

• Zero’s in lower right box discourage selection of non-association hypotheses

00inf-lnP(H1)inf

00infinf-lnP(H1)

00-lnP(H1)infinf

-lnP(H2)inf-ln[P(R3,T2|

H)P(H)]

-ln[P(R2,T2|

H)P(H)]

-ln[P(R1,T2|

H)P(H)]

inf-lnP(H2)-ln[P(R3,T1|

H)P(H)]

-ln[P(R2,T1|

H)P(H)]

-ln[P(R1,T1|

H)P(H)]

00inf-lnP(H1)inf

00infinf-lnP(H1)

00-lnP(H1)infinf

-lnP(H2)inf-ln[P(R3,T2|

H)P(H)]

-ln[P(R2,T2|

H)P(H)]

-ln[P(R1,T2|

H)P(H)]

inf-lnP(H2)-ln[P(R3,T1|

H)P(H)]

-ln[P(R2,T1|

H)P(H)]

-ln[P(R1,T1|

H)P(H)]

No Observation

Track

Initiation

or FA

Current Reports

Current

Tracks

Conversion of Association Matrix for 2-D Assignment Problem

Entity Class Tree Confidence Update With Noncommensurate Sources

P(class C| all Si , H) = {i [{P(C|Si,H)/P(C|Y(Si all i), H)} P(C| Y(Si for all i), H)] } / K {i [{P(K|Si,H)/P(K| Y(Si all i), H)}

P(K| Y(Si for all i), H)]}

if P(C|H)0 [= 0 if P(C|H)=0]

• C is the element of the fused entity class tree being updated,

• Si for each source i is its measured data [both kinematic and attribute]

• P(C|Y(Si), H) is the probability of an entity of type C given only kinematics data from source i & H, the

association hypothesis,

• K is the index of type disjoint tree classes [summed over for normalization],

• P(C|Si,H) are the entity class tree confidences based upon all measurements from each source i

Sample Interlaced Network of DF&RM Dual Level Interactions

Source

Fusion

Level 0:

Feature

Assessment

Sensor

Sensor

Sensors

Source

Fusion

Level 1:

Entity

Assessment

Fusion

Level 2:

Situation

Assessment

Fusion

Level 3:

Impact

Assessment

Management

Level 0:

Resource

Signal

Management

Management

Level 1:

Resource

Response

Management

Weapons

Mission

Management

Level 3:

Mission

Objective

Management

Management

Level 2:

Resource

Relationship

ManagementComm

Data Fusion Node Network

Resource Management Node Network

Resources

DF&RM

Reactive

Loop

DF&RM

Reactive

Loop

DF&RM

Reactive

Loop

DF&RM

Reactive

Loop

Sources

Fusion

Level 4:

System s

Assessment

Management

Level 4:

System

Management

DF&RM

Reactive

Loop

User

I/O(at all

levels

as

needed)

Data Fusion & Resource Management (DF&RM) Dual Node ......Hypothesis Evaluation Christopher Bowman,...

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